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• W2891572479 abstract "A wide range of defenses have been proposed to harden neural networks against adversarial attacks. However, a pattern has emerged in which the majority of adversarial defenses are quickly broken by new attacks. Given the lack of success at generating robust defenses, we are led to ask a fundamental question: Are adversarial attacks inevitable? This paper analyzes adversarial examples from a theoretical perspective, and identifies fundamental bounds on the susceptibility of a classifier to adversarial attacks. We show that, for certain classes of problems, adversarial examples are inescapable. Using experiments, we explore the implications of theoretical guarantees for real-world problems and discuss how factors such as dimensionality and image complexity limit a classifier's robustness against adversarial examples." @default.
• W2891572479 created "2018-09-27" @default.
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• W2891572479 date "2018-09-06" @default.
• W2891572479 modified "2023-09-25" @default.
• W2891572479 title "Are adversarial examples inevitable" @default.
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• W2891572479 hasPublicationYear "2018" @default.
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